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This article presents a tuned control algorithm for the speed and course of a four-wheeled automobile-type robot as a single nonlinear object, developed by the analytical approach of compensation for the object's dynamics and additive effects. The method is based on assessment of external effects and as a result new, advanced feedback features may appear in the control system. This approach ensures automatic movement of the object with accuracy up to a given reference filter, which is important for stable and accurate control under various conditions. In the process of the synthesis control algorithm, an inverse mathematical model of the robot was built, and reference filters were developed for a closed-loop control system through external effect channels, providing the possibility of physical implementation of the control algorithm and compensation of external effects through feedback. This combined approach allows us to take into account various effects on the robot and ensure its stable control. The developed algorithm provides control of the robot both when moving forward and backward, which expands the capabilities of maneuvering and planning motion trajectories and is especially important for robots working in confined spaces or requiring precise movement into various directions. The efficiency of the algorithm is demonstrated using a computer simulation of a closed-loop control system under various external effects. It is planned to further develop a digital algorithm for implementation on an onboard microcontroller, in order to use the new algorithm in the overall motion control system of a four-wheeled mobile robot. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders—such as stroke, amyotrophic lateral sclerosis (ALS), and spinal cord injury—by extending their movement range and thereby promoting self-independence. Brain-controlled mobile robots, however, often face challenges in safety and control performance due to the inherent limitations of BCIs. This paper proposes a shared control scheme for brain-controlled mobile robots by utilizing fuzzy logic to enhance safety, control performance, and robustness. The proposed scheme is developed by combining a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC). The SLNF controller robustly tracks the user's intentions, and the OAC ensures the safety of the mobile robot following the BCI commands. Furthermore, SLNF is a model-free controller that can learn as well as update its parameters online, diminishing the effect of disturbances. The experimental results prove the efficacy and robustness of the proposed SLNF controller including a higher task completion rate of 94.29% (compared to 79.29%, and 92.86% for Direct BCI and Fuzzy-PID, respectively), a shorter average task completion time of 85.31 s (compared to 92.01 s and 86.16 s for Direct BCI and Fuzzy-PID, respectively), and reduced settling time and overshoot. [ABSTRACT FROM AUTHOR]

Troops in the context of reconnaissance or search tasks to find out the enemy in difficult, protected and disguised terrain is an element of vulnerability that needs to be taken into account. If you insist on finding out the enemy's condition, there is a possibility that contact will occur early and cause casualties which could thwart the main task. Therefore, a tool or robot is needed to help obtain data about protected enemies by diverting the enemy's attention and providing resistance. The aim of this research is to create a prototype chainwheeled combat robot platform system that is capable of maneuvering, firing SS2 and rockets, detecting mine-prone areas, and detecting body thermals. The robot control system consists of a mobile controller and a base station. The mobile controller functions to control robot movements such as forward, backward, right turn, left turn, weapon azimuth movement, weapon elevation movement and shooting using 2.4 GHz radio control and target monitoring using FPV ((First person view) directly in the field on a frequency of 5.6 GHz, base station functions to control the robot's movement and monitor targets using a laptop via the internet. The robot system is equipped with GPS and a metal detector, so that the robot's position can be monitored and report the presence of mines to the base station. Thus, the robot created is expected to be able to help to combat duty in the context of war military operations [ABSTRACT FROM AUTHOR]

Over decades of robotics research, cooperative object transportation has been studied as a meaningful model problem for robotic networks because it possesses a variety of crucial challenges. Although these challenges are demanding, the cooperation of multiple robots has the potential to solve automation problems that are beyond the scope of an individual robot. So far, the model problem has mostly been addressed by explicitly controlling the robots' positions. However, the position-based approach suffers from some intrinsic detriments, for example, the lack of explicit feedback between robots and object. Moreover, it remains an open question how many robots shall be employed to ensure a successful transportation. This paper's purpose is to overcome these challenges using a novel force-based approach taking into account the robots' actual manipulation capabilities, that is, the exerted forces. Using cost-efficient hardware, the interaction forces are measured and, what is more, explicitly controlled by a highly responsive onboard controller. Employing a tailored software architecture, the novel force-based scheme, useful for robotic manipulation beyond the benchmark problem, is probably the most flexible of its kind regarding the number of robots and the object's shape. The controller's functionality and performance as well as the scheme's versatility are demonstrated by several hardware experiments. [ABSTRACT FROM AUTHOR]

In this article, the fault-attack control problem is investigated for wheeled mobile robot (WMR) systems subjected to actuator faults, disturbances, communication attacks, and limited communication resources. An event-observer based compensation controller is presented. With the help of the observer estimation values and the attack sleep/active instant trigger information, the tracking control performance is realized for the robot system with the assistance of the neural network approximation technology. Concretely, first, the robot system dynamic model with actuator faults, disturbances, and attacks is established. Then, an event-based proportional–integral observer (PIO) is established. In the observer framework, a state estimator, an actuator fault efficiency estimator, and a disturbance estimator are embedded. Based on the observer outputs, a second-order adaptive sliding mode fault-compensation reliable controller is presented. In this controller framework, the fault compensation, disturbance attenuation, and the attack sleep/active time instant information are contained to guarantee the reliability and performance recoverability of the robot system. Furthermore, a dynamic even condition and an adaptive trigger scheme are constructed in the sensor and actuator channel to achieve the communication-efficient purpose. Finally, two cases of the robot system are performed to verify the system recoverability of the presented approach. • A class of wheeled mobile robot systems with actuator faults, disturbances, and communication attacks is established. • An event-based proportional–integral observer framework is designed for the robot system to estimate the states, actuator faults and lumped disturbances. • A novel observer-event-based adaptive fault-attack-tolerant control architecture is presented to simultaneously preserve and recover control performance for the robot system while saving the communication resources. [ABSTRACT FROM AUTHOR]

The rapid development of network technology makes the world enter a new era, but it also brings a variety of severe information security problems, which makes the network security more and more important. Based on the existing literature and information, this paper expounds the current situation of database information system security. In view of the security risks of the media database information, this paper proposes to use the remote control system of the network robot, through sorting out the organizational structure characteristics and technology of multimedia data, establish the security defense system, and try to manage the multimedia data security. This system takes the mobile robot as the control object and adds the vision and ultrasonic transducer for the robot, so that it can carry on the semi-autonomous movement under the control of human. In addition, the host PC is used as the main control terminal to send instructions to the remote robot and receive the feedback data information. The simulation results show that the robot remote control system has good security, can ensure the establishment of database security defense technology system, and has the characteristics of convenient operation, easy to expand, and strong mobility. From the research point of view of this paper, the combination of robotics and multimedia information security is booming and will become a new application prospect. [ABSTRACT FROM AUTHOR]

This paper designed a voice interactive robot system that can conveniently execute assigned service tasks in real-life scenarios. It is equipped without a microphone where users can control the robot with spoken commands; the voice commands are then recognized by a well-trained deep neural network model of automatic speech recognition (ASR), which enables the robot to execute and complete the command based on the navigation of a real-time simultaneous localization and mapping (SLAM) algorithm. The voice interaction recognition model is divided into two parts: (1) speaker separation and (2) ASR. The speaker separation is applied by a deep-learning system consisting of eight convolution layers, one LSTM layer, and two fully connected (FC) layers to separate the speaker's voice. This model recognizes the speaker's voice as a referrer that separates and holds the required voiceprint and removes noises from other people's voiceprints. Its automatic speech recognition uses the novel sandwich-type conformer model with a stack of three layers, and combines convolution and self-attention to capture short-term and long-term interactions. Specifically, it contains a multi-head self-attention module to directly convert the voice data into text for command realization. The RGB-D vision-based camera uses a real-time appearance-based mapping algorithm to create the environment map and replace the localization with a visional odometer to allow the robot to navigate itself. Finally, the proposed ASR model was tested to check if the desired results will be obtained. Performance analysis was applied to determine the robot's environment isolation and voice recognition abilities. The results showed that the practical robot system successfully completed the interactive service tasks in a real environment. This experiment demonstrates the outstanding performance with other ASR methods and voice control mobile robot systems. It also verified that the designed voice interaction recognition system enables the mobile robot to execute tasks in real-time, showing that it is a convenient way to complete the assigned service applications. [ABSTRACT FROM AUTHOR]

The Mindstorms EV3 robot, developed by LEGO, is one of the popular robots that has been widely used in various fields. Unlike previous versions of mobile LEGO robots, EV3 allows the development of real-time applications for teaching a variety of subjects, as well as for conducting research experiments. The object of research in this case is the Mindstorms EV3 robot connected to MATLAB/Simulink. The design consists of a controller, one color sensor, two servo motors and one support wheel. Each servo motor is built on a DC collector motor with a matching gearbox and has the ability to measure the number of revolutions corresponding. A digital sensor with a sampling frequency of 1 kHz is used as a color sensor, which can determine the color or brightness of light. Despite its popularity, the EV3 robot control system in interaction with the MATLAB/Simulink programming environment is a rather complex solution and therefore requires further research. The scientific part of the research focuses on discovering the regularities of the Mindstorms EV3 control system, developing a control system model, and exploring the potential of MATLAB/Simulink to expand the robot’s capabilities. An analysis of the main elements of the control system, such as sensors and servos, was carried out. The graphs of the dependences of the characteristics of the servo drives were built and the efficiency of the robot movement was checked depending on the parameters set in the program. The result of the development of the mobile robot control system was the adjustment of the mobile robot movement regulators along a given trajectory in the form of a drawn line, which allowed estimating the maximum permissible speed of the robot movement. The presented research and development of a control system based on MATLAB/Simulink elements allows using the proposed method to control a mobile robot with high precision, analyze and verify the robot’s electromechanical parameters in real time. This control system has a high potential and can practically be integrated into industrial objects of mobile robotics, provided types the sensors and executive mechanisms of the mobile robot match. [ABSTRACT FROM AUTHOR]

This article studies the issue of modeling and tracking control for wheeled mobile robot systems with control delay. The kinematics wheeled mobile robot (WMR) is modeled under the nonholonomic constraint. Further, the state-space representations of WMR and tracking error equations with control delays are modeled. Using functional transformation, the tracking error system with control delay is converted into a delay-free one. Then, the optimal control law including a control memory compensation term is obtained by using Maximum Value Principle. The effectiveness and utility of the designed controllers, the horizontal speed and the angular speed, are verified in simulation. [ABSTRACT FROM AUTHOR]

In this paper, the control of a mobile robot system via a feedback linearization controller and anti-control of chaos with parametric uncertainty is researched. Anti-control is also applied to convert non-chaotic systems to chaotic ones and to create chaos dynamic. The synchronization of system errors with a chaotic gyroscope system is researched for energy reduction and performance improvement. In the other words, control effort is based on synchronizing the error system with chaos for decreasing control cost. The combination of these techniques yields high efficiency and global convergence of trajectories, even in the presence of parametric uncertainty, which has been shown by simulation. Finally, the energy of control signals is calculated and compared for showing the energy reduction. [ABSTRACT FROM AUTHOR]

Existing methods for path planning with obstacle avoidance need to check having the interference between a moving part and an obstacle at iteration and even to calculate their shortest distance in the case of given motion parameters. Besides, the tasks like collision-checking and minimum-distance calculating themselves are complicated and time-consuming. Rigorous mathematical analysis might be a practical way for dealing with the above-mentioned problems. An R-function is a real-valued function whose properties are fully determined by corresponding attributes of their parameters, which is usually applied to express a geometrical object. Thus, a signed distance function based on R-functions is created to represent whether two objects intervene and their level of intervention or separation. As the signed function is continuous and differentiable, the gradient information of the objective function guides a moving part to avoid its obstacles and to approach its target position rapidly. Therefore, a path planning approach with obstacle avoidance based on normalized R-functions is proposed in this paper. A discrete convex hull approach is adopted to solve the problem that R-function is inappropriate to represent a geometric object with some curves or surfaces, and pendent points and edges are generated in Boolean operations. Besides, a normalized approach ensures accuracy calculation of signed distance function. Experimental results have shown that the presented approach is a feasible way for path planning with obstacle avoidance. [ABSTRACT FROM AUTHOR]

With the increasing application of wheeled mobile robots on soft terrains, the challenge of lateral and longitudinal slippage existing in the contact surface between the wheels and the terrain has attracted more attention. To address the difficulties caused by the lateral and longitudinal slippage, this paper proposes an improved linear active disturbance rejection control (LADRC) method for path tracking control of a six-wheeled corner steering rover. Based on the LADRC, the tracking differentiator and nonlinear state error feedback are introduced into the improved LADRC. By using the improved LADRC, the influence of disturbances in inputs can be attenuated and a higher regulating efficiency than LADRC can be achieved. The simulations validate the effectiveness of the proposed approach with a good tracking performance. [ABSTRACT FROM AUTHOR]

Synchronous mobile robots formation control is one of the most challenging and interesting fields in robotics. The mobile robots communicate with each other through wireless communication to perform similar movement. This study analyzed two mobile robots that can perform synchronous movement along a shaped path. A square shape is set as a path for the mobile robot movements. The front robot being the leading robot transmits the instruction of its movement to the robot b ehind it, acting as the following robot through a wireless communication. The instruction sent by the leading robot is received by the following robot through a program embedded in the leading robot microcontroller which then drives the following robot to move and imitates the movement of the leading. The algorithm for the movement is tested on the hardware and the results of the experiment are included to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article presents the intelligent autonomous anthropomorphic wheeled mobile robotic platform motion control in unstructured environments. The fuzzy control of a wheeled autonomous anthropomorphic mobile robotic platform motion in unstructured environments with obstacles is proposed. Outputs of the fuzzy controller are the angular speed difference between the left and right wheels of the autonomous anthropomorphic robotic platform and the robot velocity. The simulation results show the effectiveness and the validity of the obstacle avoidance behaviour in the unstructured environment and velocity control of autonomous anthropomorphic mobile robotic platform motion of the proposed fuzzy control strategy. Wireless sensor-based remote control of autonomous anthropomorphic mobile robotic platform motion in unstructured environments is proposed. [ABSTRACT FROM AUTHOR]

One of the current challenges in the development of swarm robots is making them realize obstacle avoidance while keeping formation. This paper proposes a new obstacle avoidance method for multiple robots based on the aggregation and disaggregation approach. First, the formation-based multi-resolution models of swarm robots are presented and the formation attribute-based consistency mapping functions are designed. Then, obstacle avoidance behavior based on aggregation and disaggregation method is presented and the basic obstacle avoidance behaviors are designed. Finally, the simulation results are presented to show the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a novel, decentralized control algorithm to address the problem of controlling of multiple, network connected, nonholonomic mobile robots to achieve a collision-free formation and heading consensus simultaneously. Our approach transforms the formation and heading consensus problem to a pose regulation problem. The pose is derived by a virtual graph, which is an isomorphic mapping of the graph of the network connected systems. If the system is holonomic, traditional consensus on the virtual graph can stabilize the real system to the desired formation. In the case of nonholonomic systems, such as mobile robots, each robot’s pose then is regulated to the known consensus pose by a novel smooth and continuous control law. The control law also induces the real robot systems to achieve not only the desired formation but also heading consensus. We prove the proposed control schemes are globally asymptotically stable. Theoretical analysis, several simulations, and experiments have been conducted to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a method of designing variable structure control systems for robots. As the on-board robot computational resources are limited, but in some cases the demands imposed on the robot by the user are virtually limitless, the solution is to produce a variable structure system. The task dependent part has to be exchanged, however the task governs the activities of the robot. Thus not only exchange of some task-dependent modules is required, but also supervisory responsibilities have to be switched. Such control systems are necessary in the case of robot companions, where the owner of the robot may demand from it to provide many services. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel algorithm for exploring an unknown environment using a team of mobile robots. The suggested algorithm is a grid-based search method that utilizes a triangular pattern which covers an area so that exploring the whole area is guaranteed. The proposed algorithm consists of two stages. In the first stage, all the members of the team make a common triangular grid of which they are located on the vertices. In the second stage, they start exploring the area by moving between vertices of the grid. Furthermore, it is assumed that the communication range of the robots is limited, and the algorithm is based on the information of the nearest neighbours of the robots. Moreover, we apply a new mapping method employed by robots during the search operation. A mathematically rigorous proof of convergence with probability 1 of the algorithm is given. Moreover, our algorithm is implemented and simulated using a simulator of the real robots and environment and also tested via experiments with Adept Pioneer 3DX wheeled mobile robots. [ABSTRACT FROM AUTHOR]

Place recognition is an essential component of autonomous mobile robot navigation. It is used for loop-closure detection to maintain consistent maps, or to localize the robot along a route, or in kidnapped-robot situations. Camera sensors provide rich visual information for this task. We compare different approaches for visual place recognition: holistic methods (visual compass and warping), signature-based methods (using Fourier coefficients or feature descriptors (able for binary-appearance loop-closure evaluation, ABLE)), and feature-based methods (fast appearance-based mapping, FabMap). As new contributions we investigate whether warping, a successful visual homing method, is suitable for place recognition. In addition, we extend the well-known visual compass to use multiple scale planes, a concept also employed by warping. To achieve tolerance against changing illumination conditions, we examine the NSAD distance measure (normalized sum of absolute differences) on edge-filtered images. To reduce the impact of illumination changes on the distance values, we suggest to compute ratios of image distances to normalize these values to a common range. We test all methods on multiple indoor databases, as well as a small outdoor database, using images with constant or changing illumination conditions. ROC analysis (receiver-operator characteristics) and the metric distance between best-matching image pairs are used as evaluation measures. Most methods perform well under constant illumination conditions, but fail under changing illumination. The visual compass using the NSAD measure on edge-filtered images with multiple scale planes, while being slower than signature methods, performs best in the latter case. [ABSTRACT FROM AUTHOR]

Aggregation is one of the most fundamental behaviors and has been studied in swarm robotic researches for more than two decades. Studies in biology have revealed that the environment is a preeminent factor, especially in cue-based aggregation. This can be defined as aggregation at a particular location which is a heat or a light source acting as a cue indicating an optimal zone. In swarm robotics, studies on cue-based aggregation mainly focused on different methods of aggregation and different parameters such as population size. Although of utmost importance, environmental effects on aggregation performance have not been studied systematically. In this paper, we study the effects of different environmental factors: size, texture and number of cues in a static setting, and moving cues in a dynamic setting using real robots. We used the aggregation time and size of the aggregate as the two metrics with which to measure aggregation performance. We performed real robot experiments with different population sizes and evaluated the performance of aggregation using the defined metrics. We also proposed a probabilistic aggregation model and predicted the aggregation performance accurately in most of the settings. The results of the experiments show that environmental conditions affect the aggregation performance considerably and have to be studied in depth. [ABSTRACT FROM AUTHOR]

This article approaches the problem of controlling quadrupedal running and jumping motions with a parameterized, model-based, state-feedback controller. Inspired by the motor learning principles observed in nature, our method automatically fine tunes the parameters of our controller by repeatedly executing slight variations of the same motion task. This learn-through-practice process is performed in simulation to best exploit computational resources and to prevent the robot from damaging itself. To ensure that the simulation results match the behavior of the hardware platform, we introduce and validate an accurate model of the compliant actuation system. The proposed method is experimentally verified on the torque-controllable quadruped robot StarlETH by executing squat jumps and dynamic gaits, such as a running trot, pronk, and a bounding gait. [ABSTRACT FROM PUBLISHER]

This study is concerned with the leader-follower formation control of non-holonomic mobile robots without communication. A pan-controlled camera is the sole sensor used by the follower to observe the leader. A new visionbased formation system without range states is proposed, which eliminates the need of inter-robot range estimation. An adaptive formation controller and an adaptive camera controller are designed in the presence of unknown leader's velocities and vision-related parameters. A parameter projection algorithm is integrated into the controllers to bound the control inputs. The camera controller ensures the stable observation of the leader's features. Thus the camera constantly provides visual measurements for the formation controller which guarantees the formation maintenance. Simulation and experimental results verify the effectiveness of the proposed active vision-based formation control approach. [ABSTRACT FROM AUTHOR]

This paper presents a novel 3-D servoing approach using dense depth maps to perform robotic tasks. With respect to position-based approaches, our method does not require the estimation of the 3-D pose (direct), nor the extraction and matching of 3-D features (dense), and only requires dense depth maps provided by 3-D sensors. Our approach has been validated in various servoing experiments using the depth information from a low-cost Red Green Blue-Depth (RGB-D) sensor. Positioning tasks are properly achieved despite noisy measurements, even when partial occlusions or scene modifications occur. We also show that, in cases where a reference depth map cannot be easily available, synthetic ones generated with a rendering engine still lead to satisfactory positioning performances. Application of the approach to the navigation of a mobile robot is also demonstrated. [ABSTRACT FROM AUTHOR]

In this paper, we present the synthesis and design of a rotary-to-linear continuously variable transmission with the ability to passively change gear ratio as a function of the output load. The primary mechanism involves variable-pitch rollers whose angle changes as a function of the output load due to the compliance properties of their housing. By changing spring stiffness, the relationship between the linear output load and transmission ratio can be tuned to optimize drive motor operating conditions over the entire range of output loads. After laying out the working concept, we show the performance analysis for such a transmission applied to a 6-W DC motor and present an example design analysis for tuning to maximize power output over the entire range of operating conditions. A prototype system was used to measure key parameters such as rolling resistance and lateral slip coefficients and to evaluate the transmission performance in a target application. [ABSTRACT FROM AUTHOR]

Because the collision avoidance function is indispensable for providing safe and easy operation of human-operated robotic systems, this paper deals with the collision avoidance control for a human-operated mobile robot in unknown environments. A typical four-wheeled mobile robot with infrared distance sensors for detecting obstacles is considered. The robot cannot move in an arbitrary direction owing to a nonholonomic constraint. Therefore, we propose a simple control approach in which a human operator’s control input is modified in real time to satisfy the nonholonomic constraint and avoid collision with obstacles. The proposed controller has steering- and brake-like functions that are adjusted according to the distance sensor information. The stability of the proposed control system is analyzed with a linear model. The effectiveness of the proposed method is confirmed by experiments in which several operators control the robot in an environment with obstacles. [ABSTRACT FROM AUTHOR]

This paper presents an information potential method for integrated path planning and control. The method is applicable to unicycle robotic sensors deployed to classify multiple targets in an obstacle-populated environment. A new navigation function, referred to as information potential, is generated from the target conditional mutual information, and used to design a closed-loop stable switched control law. The information potential is shown to obey the properties of potential navigation functions and to enable measurements that maximize the information value over time. The information potential is also used to construct a local roadmap for escaping local minima. The properties and computational complexity of the local roadmap algorithm are analyzed. Numerical simulation results show that the method outperforms other strategies, such as rapidly exploring random trees and classical potential field methods. [ABSTRACT FROM PUBLISHER]

Many applications in mobile robotics require the localization of the robot with respect to the boundary of a path (e.g., a lane marker) and the reconstruction of the path in front of the robot. The former can be used as a basis for a reactive control scheme that drives the robot along the specified path and the latter is used to plan its future motion. Our solution to these problems relies on a camera mounted on the chassis and pointing headway. The relevant elements of the image grabbed from the camera (i.e., the lines delimiting the path) are robustly extracted and projected into the field-of-view of a virtual camera looking over the scene from above. This way, we obtain a top plan view that allows us to reconstruct position and bearing of the vehicle irrespective of mechanical vibrations or imperfect plane of motion. What is more, by pushing forward the virtual camera, we are able to reconstruct the path in front of the robot for some distance ahead. In this paper, we describe the main ideas underlying the approach and its implementation. The accuracy of the technique and the computational workload is evaluated through a large set of experiments. [ABSTRACT FROM PUBLISHER]

This paper aims to investigate the formation control of multi-robot systems, where the kinematic model of a differentially driven wheeled mobile robot is considered. Based on the graph-theoretic concepts and locally distributed information, an adaptive neural fuzzy formation controller is designed with the capability of on-line learning. The learning rules of controller parameters can be derived from the analyzing of Lyapunov stability. In addition to simulations, the proposed techniques are applied to an experimental multi-robot platform for performance validations. From simulation and experimental results, the proposed adaptive neural fuzzy protocol can provide better formation responses compared to conventional consensus algorithms. [ABSTRACT FROM AUTHOR]